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Proceedings Paper

Performance of a fault detector artificial neural network using different paradigms
Author(s): Mo-yuen Chow; Aaron V. Chew; Sui-Oi Yee
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Paper Abstract

Fault detection and diagnosis are important issues in engineering. With proper fault detection and diagnosis schemes, factors such as safety, efficiency, and cost of system operations can be significantly improved. This paper presents the use of two popular artificial neural network paradigms, namely feedforward networks and Kohonen nets (trained by Learning Vector Quantization algorithm), to perform fault detection. For illustration purposes, the fault detection of single-phase squirrel-cage induction motors is discussed. Comparisons of the preliminary results obtained from the feedforward net and Kohonen net to perform single- phase induction motor fault detection, in terms of factors such as classification accuracy and training time, are presented and discussed.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139974
Show Author Affiliations
Mo-yuen Chow, North Carolina State Univ. (United States)
Aaron V. Chew, North Carolina State Univ. (United States)
Sui-Oi Yee, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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